In recent years, Business Intelligence (BI) tools have become increasingly important for large business enterprises and other organizations. BI tools are often used to create simulation models and predictions in order to support better business decision making. Typically, BI tools use data stored in a database for creating simulation models and predictions for an object. The data may be used for determining distribution function of the object. The distribution function is a function that represents the distribution of an object. Based on the determined distribution function of the object, BI tools predict future values of the object or simulate the object. For correctly predicting values or simulating an object, the BI tool may require raw distribution function of the object. A raw distribution function of the object represents the distribution of the object without the influence of any other object. The raw distribution of the object in turn is dependent on availability of raw data corresponding to the object at a desired granularity.
Enterprise applications demand and generate vast amounts of data during a typical business cycle. Data may include transaction data, statistical data, and some other secondary data. Due to memory constraints of a database, only portions of this data are stored in a database, for example compliance related data or data related to analytics may be stored in the database whereas other data may not be stored at all. Furthermore, analytical data may also be aggregated in a pre-defined manner to save database resources. Due to these practices, the raw data of the object may not be available at a desired granularity for determining the raw distribution function of the object. This may lead to limited analytical and simulation possibilities as the determined distribution function may not correctly predict or simulate values of the object.